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Yin S, Ding N, Ji Y, Qiao Z, Yuan J, Chi J, Jin L. The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions. Acta Radiol 2024; 65:554-564. [PMID: 38623640 DOI: 10.1177/02841851241245970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
BACKGROUND Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery. PURPOSE To investigate the potential of various machine learning models, incorporating radiomics and deep transfer learning, in predicting the nature of cholesterol and adenomatous gallbladder polyps. MATERIAL AND METHODS A retrospective analysis was conducted on clinical and imaging data from 100 patients with cholesterol or adenomatous polyps confirmed by surgery and pathology at our hospital between September 2015 and February 2023. Preoperative contrast-enhanced CT radiomics combined with deep learning features were utilized, and t-tests and least absolute shrinkage and selection operator (LASSO) cross-validation were employed for feature selection. Subsequently, 11 machine learning algorithms were utilized to construct prediction models, and the area under the ROC curve (AUC), accuracy, and F1 measure were used to assess model performance, which was validated in a validation group. RESULTS The Logistic algorithm demonstrated the most effective prediction in identifying polyp properties based on 10 radiomics combined with deep learning features, achieving the highest AUC (0.85 in the validation group, 95% confidence interval = 0.68-1.0). In addition, the accuracy (0.83 in the validation group) and F1 measure (0.76 in the validation group) also indicated strong performance. CONCLUSION The machine learning radiomics combined with deep learning model based on enhanced CT proves valuable in predicting the characteristics of cholesterol and adenomatous gallbladder polyps. This approach provides a more reliable basis for preoperative diagnosis and treatment of these conditions.
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Affiliation(s)
- Shengnan Yin
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Ning Ding
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Yiding Ji
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Zhenguo Qiao
- Department of Gastroenterology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Jianmao Yuan
- Department of General Surgery, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Jing Chi
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
| | - Long Jin
- Department of Radiology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, PR China
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Ahrari S, Zaragori T, Zinsz A, Oster J, Imbert L, Verger A. Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma. Sci Rep 2024; 14:3256. [PMID: 38332004 PMCID: PMC10853227 DOI: 10.1038/s41598-024-53693-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/03/2024] [Indexed: 02/10/2024] Open
Abstract
This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[18F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744-0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716-0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548-0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512-0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts.
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Affiliation(s)
- Shamimeh Ahrari
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France
| | - Timothée Zaragori
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France
| | - Adeline Zinsz
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, 54000, Nancy, France
| | - Julien Oster
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France
| | - Laetitia Imbert
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, 54000, Nancy, France
| | - Antoine Verger
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, 54000, Nancy, France.
- Nancyclotep Imaging Platform, Université de Lorraine, 54000, Nancy, France.
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, 54000, Nancy, France.
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Connor K, Conroy E, White K, Shiels LP, Keek S, Ibrahim A, Gallagher WM, Sweeney KJ, Clerkin J, O'Brien D, Cryan JB, O'Halloran PJ, Heffernan J, Brett F, Lambin P, Woodruff HC, Byrne AT. A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring. Sci Rep 2024; 14:2720. [PMID: 38302657 PMCID: PMC10834979 DOI: 10.1038/s41598-024-52960-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/25/2024] [Indexed: 02/03/2024] Open
Abstract
Here, we establish a CT-radiomics based method for application in invasive, orthotopic rodent brain tumour models. Twenty four NOD/SCID mice were implanted with U87R-Luc2 GBM cells and longitudinally imaged via contrast enhanced (CE-CT) imaging. Pyradiomics was employed to extract CT-radiomic features from the tumour-implanted hemisphere and non-tumour-implanted hemisphere of acquired CT-scans. Inter-correlated features were removed (Spearman correlation > 0.85) and remaining features underwent predictive analysis (recursive feature elimination or Boruta algorithm). An area under the curve of the receiver operating characteristic curve was implemented to evaluate radiomic features for their capacity to predict defined outcomes. Firstly, we identified a subset of radiomic features which distinguish the tumour-implanted hemisphere and non- tumour-implanted hemisphere (i.e, tumour presence from normal tissue). Secondly, we successfully translate preclinical CT-radiomic pipelines to GBM patient CT scans (n = 10), identifying similar trends in tumour-specific feature intensities (E.g. 'glszm Zone Entropy'), thereby suggesting a mouse-to-human species conservation (a conservation of radiomic features across species). Thirdly, comparison of features across timepoints identify features which support preclinical tumour detection earlier than is possible by visual assessment of CT scans. This work establishes robust, preclinical CT-radiomic pipelines and describes the application of CE-CT for in-depth orthotopic brain tumour monitoring. Overall we provide evidence for the role of pre-clinical 'discovery' radiomics in the neuro-oncology space.
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Affiliation(s)
- Kate Connor
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York Street, Dublin 2, Ireland
- National Pre-Clinical Imaging Centre (NPIC), Dublin, Ireland
| | - Emer Conroy
- National Pre-Clinical Imaging Centre (NPIC), Dublin, Ireland
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin, Ireland
| | - Kieron White
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York Street, Dublin 2, Ireland
- National Pre-Clinical Imaging Centre (NPIC), Dublin, Ireland
| | - Liam P Shiels
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York Street, Dublin 2, Ireland
- National Pre-Clinical Imaging Centre (NPIC), Dublin, Ireland
| | - Simon Keek
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Abdalla Ibrahim
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - William M Gallagher
- National Pre-Clinical Imaging Centre (NPIC), Dublin, Ireland
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin, Ireland
| | | | - James Clerkin
- Department of Neurosurgery, Beaumont Hospital, Dublin, Ireland
| | - David O'Brien
- Department of Neurosurgery, Beaumont Hospital, Dublin, Ireland
| | - Jane B Cryan
- Department of Neurosurgery, Queen Elizabeth Hospital, Birmingham, UK
| | - Philip J O'Halloran
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York Street, Dublin 2, Ireland
- Department of Neurosurgery, Queen Elizabeth Hospital, Birmingham, UK
| | | | - Francesca Brett
- Department of Neuropathology, Beaumont Hospital, Dublin, Ireland
| | - Philippe Lambin
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Annette T Byrne
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York Street, Dublin 2, Ireland.
- National Pre-Clinical Imaging Centre (NPIC), Dublin, Ireland.
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin, Ireland.
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Tan D, Mohamad Salleh SA, Manan HA, Yahya N. Delta-radiomics-based models for toxicity prediction in radiotherapy: A systematic review and meta-analysis. J Med Imaging Radiat Oncol 2023; 67:564-579. [PMID: 37309680 DOI: 10.1111/1754-9485.13546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/28/2023] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Delta-radiomics models are potentially able to improve the treatment assessment than single-time point features. The purpose of this study is to systematically synthesize the performance of delta-radiomics-based models for radiotherapy (RT)-induced toxicity. METHODS A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included. RESULTS Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively. CONCLUSION Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.
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Affiliation(s)
- Daryl Tan
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | | | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Noorazrul Yahya
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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Zeng Q, Ke M, Zhong L, Zhou Y, Zhu X, He C, Liu L. Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy. Acad Radiol 2023; 30:1638-1647. [PMID: 36564256 DOI: 10.1016/j.acra.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/31/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics at baseline and after two cycles of neoadjuvant therapy (NAT) and associated longitudinal changes for early prediction of the NAT response in patients with breast cancer. MATERIALS AND METHODS One hundred seventeen patients with breast cancer who underwent DCE-MRI before NAT and after two cycles of NAT from April 2019 to November 2021 were enrolled retrospectively. Patients were randomly divided into a training set (n = 81) and a test set (n = 36) at a ratio of 7:3. Clinical-pathological data and the relative tumor maximum diameter regression value (diameter%) were also collected. A total of 851 radiomic features were extracted from the phase with the most pronounced tumor enhancement on DCE-MRI T1 imaging acquired both pre- and post-treatment. Delta and delta% radiomics features were also calculated. The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to select features, and a logistic regression model was used to calculate pre-NAT, early-NAT, delta, and delta% radscores and then select among four radscores to build a Fusion radiomics model. The final clinical-radiomics model was constructed by combining fusion radscores and clinical-pathological variables. The discrimination and clinical utility of the models were further evaluated and compared. RESULTS The area under the curve (AUC) values of the fusion radiomics model based on pre-NAT, Delta, and Delta% radscores were 0.868 of 0.825. The clinical-radiomics model integrating Fusion radscores and clinical-pathological variables achieved AUC values of 0.920 of 0.884, which were higher than those of the clinical model constructed by AUC values (0.858/0.831), although no significant improvement was observed in the test set (Delong test, p = 0.196). Decision curve analysis (DCA) showed that the clinical-radiomics model demonstrated more clinical utility than the clinical model. CONCLUSION DCE-MRI-based radiomics features may have potential for pathological complete response (pCR) prediction in the early phase of NAT. By combining radiomics features and clinical-pathological characteristics, higher diagnostic performance can be achieved.
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Affiliation(s)
- Qiao Zeng
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Mengmeng Ke
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Linhua Zhong
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Yongjie Zhou
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Xuechao Zhu
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Chongwu He
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.)
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, No. 519, Beijing East Road, Qingshanhu District Nanchang, Jiangxi Province, 330029, China (Q.Z., M.K., L.Z., Y.C., X.Z., L.L.); Department of Breast Surgery, Jiangxi Cancer Hospital, Nanchang, China (C.H.).
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Kim SJ, Choi JY, Ahn YC, Ahn MJ, Moon SH. The prognostic value of radiomic features from pre- and post-treatment 18F-FDG PET imaging in patients with nasopharyngeal carcinoma. Sci Rep 2023; 13:8462. [PMID: 37231092 DOI: 10.1038/s41598-023-35582-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 05/20/2023] [Indexed: 05/27/2023] Open
Abstract
Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose (FDG) is widely used for management of nasopharyngeal carcinoma (NPC). Combining the radiomic features of pre- and post-treatment FDG PET images may improve tumor characterization and prognostic predication. We investigated prognostic value of radiomic features from pre- and post-radiotherapy FDG PET images in patients with NPC. Quantitative radiomic features of primary tumors were extracted from the FDG PET images of 145 NPC patients and the delta values were also calculated. The study population was divided randomly into two groups, the training and test sets (7:3). A random survival forest (RSF) model was adopted to perform analyses of progression-free survival (PFS) and overall survival (OS). There were 37 (25.5%) cases of recurrence and 16 (11.0%) cases of death during a median follow-up period of 54.5 months. Both RSF models with clinical variables and radiomic PET features for PFS and OS showed comparable predictive performance to RSF models with clinical variables and conventional PET parameters. Tumoral radiomic features of pre- and post-treatment FDG PET and the corresponding delta values may predict PFS and OS in patients with NPC.
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Affiliation(s)
- Soo Jeong Kim
- Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Yong Chan Ahn
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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Barge P, Oevermann A, Maiolini A, Durand A. Machine learning predicts histologic type and grade of canine gliomas based on MRI texture analysis. Vet Radiol Ultrasound 2023. [PMID: 37133981 DOI: 10.1111/vru.13242] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 05/04/2023] Open
Abstract
Conventional MRI features of canine gliomas subtypes and grades significantly overlap. Texture analysis (TA) quantifies image texture based on spatial arrangement of pixel intensities. Machine learning (ML) models based on MRI-TA demonstrate high accuracy in predicting brain tumor types and grades in human medicine. The aim of this retrospective, diagnostic accuracy study was to investigate the accuracy of ML-based MRI-TA in predicting canine gliomas histologic types and grades. Dogs with histopathological diagnosis of intracranial glioma and available brain MRI were included. Tumors were manually segmented across their entire volume in enhancing part, non-enhancing part, and peri-tumoral vasogenic edema in T2-weighted (T2w), T1-weighted (T1w), FLAIR, and T1w postcontrast sequences. Texture features were extracted and fed into three ML classifiers. Classifiers' performance was assessed using a leave-one-out cross-validation approach. Multiclass and binary models were built to predict histologic types (oligodendroglioma vs. astrocytoma vs. oligoastrocytoma) and grades (high vs. low), respectively. Thirty-eight dogs with a total of 40 masses were included. Machine learning classifiers had an average accuracy of 77% for discriminating tumor types and of 75.6% for predicting high-grade gliomas. The support vector machine classifier had an accuracy of up to 94% for predicting tumor types and up to 87% for predicting high-grade gliomas. The most discriminative texture features of tumor types and grades appeared related to the peri-tumoral edema in T1w images and to the non-enhancing part of the tumor in T2w images, respectively. In conclusion, ML-based MRI-TA has the potential to discriminate intracranial canine gliomas types and grades.
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Affiliation(s)
- Pablo Barge
- Division of Clinical Radiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Anna Oevermann
- Division of Neurological Sciences, Department of Clinical Research and Veterinary Public Health, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Arianna Maiolini
- Division of Clinical Neurology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Alexane Durand
- Division of Clinical Radiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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Peng J, Wang W, Jin H, Qin X, Hou J, Yang Z, Shu Z. Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning. BMC Cancer 2023; 23:365. [PMID: 37085830 PMCID: PMC10120125 DOI: 10.1186/s12885-023-10855-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/17/2023] [Indexed: 04/23/2023] Open
Abstract
OBJECTIVE In this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space-time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients. METHODS Clinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC). RESULTS The AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05). CONCLUSIONS The RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.
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Affiliation(s)
- Jiaxuan Peng
- Jinzhou medical university, Jinzhou, Liaoning Province, China
| | - Wei Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Hui Jin
- Bengbu medical college, Bengbu, China
| | - Xue Qin
- Bengbu medical college, Bengbu, China
| | - Jie Hou
- Jinzhou medical university, Jinzhou, Liaoning Province, China
| | - Zhang Yang
- Center for General Practice Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Center for General Practice Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
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Joseph N, Benetz BA, Chirra P, Menegay H, Oellerich S, Baydoun L, Melles GRJ, Lass JH, Wilson DL. Machine Learning Analysis of Postkeratoplasty Endothelial Cell Images for the Prediction of Future Graft Rejection. Transl Vis Sci Technol 2023; 12:22. [PMID: 36790821 PMCID: PMC9940770 DOI: 10.1167/tvst.12.2.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
Abstract
Purpose This study developed machine learning (ML) classifiers of postoperative corneal endothelial cell images to identify postkeratoplasty patients at risk for allograft rejection within 1 to 24 months of treatment. Methods Central corneal endothelium specular microscopic images were obtained from 44 patients after Descemet membrane endothelial keratoplasty (DMEK), half of whom had experienced graft rejection. After deep learning segmentation of images from all patients' last and second-to-last imaging, time points prior to rejection were analyzed (175 and 168, respectively), and 432 quantitative features were extracted assessing cellular spatial arrangements and cell intensity values. Random forest (RF) and logistic regression (LR) models were trained on novel-to-this-application features from single time points, delta-radiomics, and traditional morphometrics (endothelial cell density, coefficient of variation, hexagonality) via 10 iterations of threefold cross-validation. Final assessments were evaluated on a held-out test set. Results ML classifiers trained on novel-to-this-application features outperformed those trained on traditional morphometrics for predicting future graft rejection. RF and LR models predicted post-DMEK patients' allograft rejection in the held-out test set with >0.80 accuracy. RF models trained on novel features from second-to-last time points and delta-radiomics predicted post-DMEK patients' rejection with >0.70 accuracy. Cell-graph spatial arrangement, intensity, and shape features were most indicative of graft rejection. Conclusions ML classifiers successfully predicted future graft rejections 1 to 24 months prior to clinically apparent rejection. This technology could aid clinicians to identify patients at risk for graft rejection and guide treatment plans accordingly. Translational Relevance Our software applies ML techniques to clinical images and enhances patient care by detecting preclinical keratoplasty rejection.
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Affiliation(s)
- Naomi Joseph
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Beth Ann Benetz
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, OH, USA,Cornea Image Analysis Reading Center, Cleveland, OH, USA
| | - Prathyush Chirra
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Harry Menegay
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, OH, USA,Cornea Image Analysis Reading Center, Cleveland, OH, USA
| | - Silke Oellerich
- Netherlands Institute for Innovative Ocular Surgery (NIIOS), Rotterdam, The Netherlands
| | - Lamis Baydoun
- Netherlands Institute for Innovative Ocular Surgery (NIIOS), Rotterdam, The Netherlands,University Eye Hospital Münster, Münster, Germany,ELZA Institute Dietikon/Zurich, Zurich, Switzerland
| | - Gerrit R. J. Melles
- Netherlands Institute for Innovative Ocular Surgery (NIIOS), Rotterdam, The Netherlands,NIIOS-USA, San Diego, CA, USA
| | - Jonathan H. Lass
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, OH, USA,Cornea Image Analysis Reading Center, Cleveland, OH, USA
| | - David L. Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
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10
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García-García S, García-Galindo M, Arrese I, Sarabia R, Cepeda S. Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121746. [PMID: 36556948 PMCID: PMC9786785 DOI: 10.3390/medicina58121746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor's biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM.
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Affiliation(s)
- Sergio García-García
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
- Correspondence:
| | - Manuel García-Galindo
- Faculty of Medicine, University of Valladolid, Avenida Ramón y Cajal 7, 47003 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Santiago Cepeda
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
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11
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Chen R, Fu Y, Yi X, Pei Q, Zai H, Chen BT. Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges. JOURNAL OF ONCOLOGY 2022; 2022:1590620. [PMID: 36471884 PMCID: PMC9719428 DOI: 10.1155/2022/1590620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/30/2022] [Accepted: 11/09/2022] [Indexed: 08/01/2023]
Abstract
Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.
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Affiliation(s)
- Rui Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Qian Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Hongyan Zai
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Bihong T. Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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12
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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13
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Qin Y, Zhu LH, Zhao W, Wang JJ, Wang H. Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer. Front Oncol 2022; 12:913683. [PMID: 36016617 PMCID: PMC9395725 DOI: 10.3389/fonc.2022.913683] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/23/2022] [Indexed: 12/20/2022] Open
Abstract
By breaking the traditional medical image analysis framework, precision medicine–radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially 125I CT-guided radioactive seed implant brachytherapy.
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Affiliation(s)
- Yun Qin
- School of Physics, Beihang University, Beijing, China
| | - Li-Hua Zhu
- School of Physics, Beihang University, Beijing, China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, China
| | - Jun-Jie Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
- *Correspondence: Jun-Jie Wang, ; Hao Wang,
| | - Hao Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
- *Correspondence: Jun-Jie Wang, ; Hao Wang,
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14
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Xu J, Meng Y, Qiu K, Topatana W, Li S, Wei C, Chen T, Chen M, Ding Z, Niu G. Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges. Front Oncol 2022; 12:892056. [PMID: 35965542 PMCID: PMC9363668 DOI: 10.3389/fonc.2022.892056] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/22/2022] [Indexed: 12/24/2022] Open
Abstract
Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.
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Affiliation(s)
- Jiaona Xu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuting Meng
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kefan Qiu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Win Topatana
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shijie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wei
- Department of Neurology, Affiliated Ningbo First Hospital, Ningbo, China
| | - Tianwen Chen
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mingyu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Guozhong Niu
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
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15
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Zhang J, Yang C, Gong C, Zhou Y, Li C, Li F. Magnetic resonance imaging parameter-based machine learning for prognosis prediction of high-intensity focused ultrasound ablation of uterine fibroids. Int J Hyperthermia 2022; 39:835-846. [PMID: 35764325 DOI: 10.1080/02656736.2022.2090622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
Abstract
Objectives: To develop and apply magnetic resonance imaging (MRI) parameter-based machine learning (ML) models to predict non-perfused volume (NPV) reduction and residual regrowth of uterine fibroids after high-intensity focused ultrasound (HIFU) ablation.Methods: MRI data of 573 uterine fibroids in 410 women who underwent HIFU ablation from the Chongqing Haifu Hospital (training set, N = 405) and the First Affiliated Hospital of Chongqing Medical University (testing set, N = 168) were retrospectively analyzed. Fourteen MRI parameters were screened for important predictors using the Boruta algorithm. Multiple ML models were constructed to predict NPV reduction and residual fibroid regrowth in a median of 203.0 (interquartile range: 122.5-367.5) days. Furthermore, optimal models were used to plot prognostic prediction curves.Results: Fourteen features, including postoperative NPV, indicated predictive ability for NPV reduction. Based on the 10-fold cross-validation, the best average performance of multilayer perceptron achieved with R2 was 0.907. In the testing set, the best model was linear regression (R2 =0.851). Ten features, including the maximum thickness of residual fibroids, revealed predictive power for residual fibroid regrowth. Random forest model achieved the best performance with an average area under the curve (AUC) of 0.904 (95% confidence interval (CI), 0.869-0.939), which was maintained in the testing set with an AUC of 0.891 (95% CI, 0.850-0.929).Conclusions: ML models based on MRI parameters can be used for prognostic prediction of uterine fibroids after HIFU ablation. They can potentially serve as a new method for learning more about ablated fibroids.
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Affiliation(s)
- Jinwei Zhang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Chao Yang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Chunmei Gong
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Ye Zhou
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Chenghai Li
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Faqi Li
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China
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16
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Jian A, Liu S, Di Ieva A. Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging. Neurosurgery 2022; 91:8-26. [PMID: 35348129 DOI: 10.1227/neu.0000000000001938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/08/2022] [Indexed: 12/30/2022] Open
Abstract
Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.
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Affiliation(s)
- Anne Jian
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Royal Melbourne Hospital, Melbourne, Australia
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
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17
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Mazaheri Y, Thakur SB, Bitencourt AGV, Lo Gullo R, Hötker AM, Bates DDB, Akin O. Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods. BJR Open 2022; 4:20210072. [PMID: 36105425 PMCID: PMC9459949 DOI: 10.1259/bjro.20210072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow.
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Affiliation(s)
| | | | | | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Andreas M. Hötker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - David D B Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
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18
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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19
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Speckter H, Radulovic M, Trivodaliev K, Vranes V, Joaquin J, Hernandez W, Mota A, Bido J, Hernandez G, Rivera D, Suazo L, Valenzuela S, Stoeter P. MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery. J Neurooncol 2022; 159:281-291. [PMID: 35715668 DOI: 10.1007/s11060-022-04063-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/07/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE This report presents the first investigation of the radiomics value in predicting the meningioma volumetric response to gamma knife radiosurgery (GKRS). METHODS The retrospective study included 93 meningioma patients imaged by three Tesla MRI. Tumor morphology was quantified by calculating 337 shape, first- and second-order radiomic features from MRI obtained before GKRS. Analysis was performed on original 3D MR images and after their laplacian of gaussian (LoG), logarithm and exponential filtering. The prediction performance was evaluated by Pearson correlation, linear regression and ROC analysis, with meningioma volume change per month as the outcome. RESULTS Sixty calculated features significantly correlated with the outcome. The feature selection based on LASSO and multivariate regression started from all available 337 radiomic and 12 non-radiomic features. It selected LoG-sigma-1-0-mm-3D_firstorder_InterquartileRange and logarithm_ngtdm_Busyness as the predictively most robust and non-redundant features. The radiomic score based on these two features produced an AUC = 0.81. Adding the non-radiomic karnofsky performance status (KPS) to the score has increased the AUC to 0.88. Low values of the radiomic score defined a homogeneous subgroup of 50 patients with consistent absence (0%) of tumor progression. CONCLUSION This is the first report of a strong association between MRI radiomic features and volumetric meningioma response to radiosurgery. The clinical importance of the early and reliable prediction of meningioma responsiveness to radiosurgery is based on its potential to aid individualized therapy decision making.
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Affiliation(s)
- Herwin Speckter
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic.
| | - Marko Radulovic
- Department of Experimental Oncology, Institute for Oncology & Radiology of Serbia, Pasterova 14, 11000, Belgrade, Serbia
| | | | - Velicko Vranes
- Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic
| | - Johanna Joaquin
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Wenceslao Hernandez
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Angel Mota
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Jose Bido
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Giancarlo Hernandez
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Diones Rivera
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Luis Suazo
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Santiago Valenzuela
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Peter Stoeter
- Centro Gamma Knife Dominicano and Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
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20
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Li C, Nie Z, Yang X. Splitting proximate algorithm for deformable image registration based on functions of bounded deformation. Med Phys 2022; 49:5149-5159. [PMID: 35674467 DOI: 10.1002/mp.15721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 02/15/2022] [Accepted: 03/10/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Deformable image registration is a crucial task in the field of medical image analysis. Functions of bounded deformation (BD) have been proved effective for modeling the displacement fields between medical images since they can capture the discontinuity of displacement fields along edges of organs and tissues. PURPOSE However, we find that at the same time, BD functions-based models tend to obtain discontinuous displacement fields inside the regions of organs and tissues due to image noises in some cases and the presented gradient descent algorithm is time-consuming. To alleviate these problems, we propose a faster algorithm named SPA: splitting proximate algorithm. METHODS In the framework of variable-splitting scheme, we incorporate a proximal term in the deformable registration energy based on functions of BD. RESULTS The proposed algorithm can efficiently solve the original model and obtain displacement fields, which look more natural and plausible. Numerical experiments show the effectiveness and stability of the proposed algorithm. CONCLUSIONS The proposed SPA is able to drastically register the images with a plausible deformation field and not sensitive to noise.
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Affiliation(s)
- Chen Li
- Department of Mathematics, Nanjing University, Nanjing, P.R. China
| | - Ziwei Nie
- Department of Mathematics, Nanjing University, Nanjing, P.R. China
| | - Xiaoping Yang
- Department of Mathematics, Nanjing University, Nanjing, P.R. China
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21
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Xi Y, Ge X, Ji H, Wang L, Duan S, Chen H, Wang M, Hu H, Jiang F, Ding Z. Prediction of Response to Induction Chemotherapy Plus Concurrent Chemoradiotherapy for Nasopharyngeal Carcinoma Based on MRI Radiomics and Delta Radiomics: A Two-Center Retrospective Study. Front Oncol 2022; 12:824509. [PMID: 35530350 PMCID: PMC9074388 DOI: 10.3389/fonc.2022.824509] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/23/2022] [Indexed: 12/03/2022] Open
Abstract
Objective We aimed to establish an MRI radiomics model and a Delta radiomics model to predict tumor retraction after induction chemotherapy (IC) combined with concurrent chemoradiotherapy (CCRT) for primary nasopharyngeal carcinoma (NPC) in non-endemic areas and to validate its efficacy. Methods A total of 272 patients (155 in the training set, 66 in the internal validation set, and 51 in the external validation set) with biopsy pathologically confirmed primary NPC who were screened for pretreatment MRI were retrospectively collected. The NPC tumor was delineated as a region of interest in the two sequenced images of MRI before treatment and after IC, followed by radiomics feature extraction. With the use of maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, logistic regression was performed to establish pretreatment MRI radiomics and pre- and post-IC Delta radiomics models. The optimal Youden’s index was taken; the receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drawn to evaluate the predictive efficacy of different models. Results Seven optimal feature subsets were selected from the pretreatment MRI radiomics model, and twelve optimal subsets were selected from the Delta radiomics model. The area under the ROC curve, accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of the MRI radiomics model were 0.865, 0.827, 0.837, 0.813, 0.776, and 0.865, respectively; the corresponding indicators of the Delta radiomics model were 0.941, 0.883, 0.793, 0.968, 0.833, and 0.958, respectively. Conclusion The pretreatment MRI radiomics model and pre- and post-IC Delta radiomics models could predict the IC-CCRT response of NPC in non-epidemic areas.
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Affiliation(s)
- Yuzhen Xi
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, 903rd Hospital of PLA, Hangzhou, China
| | - Xiuhong Ge
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
| | - Haiming Ji
- Department of Radiology, Liangzhu Hospital, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Haonan Chen
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Mengze Wang
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital Affiliated to Medical College Zhejiang University, Hangzhou, China
| | - Feng Jiang
- Department of Head and Neck Radiotherapy, Zhejiang Cancer Hospital/Zhejiang Province Key Laboratory of Radiation Oncology, Hangzhou, China
- *Correspondence: Feng Jiang, ; Zhongxiang Ding,
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Feng Jiang, ; Zhongxiang Ding,
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22
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Mireștean CC, Volovăț C, Iancu RI, Iancu DPT. Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease. J Clin Med 2022; 11:jcm11030616. [PMID: 35160069 PMCID: PMC8836903 DOI: 10.3390/jcm11030616] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/23/2022] [Accepted: 01/24/2022] [Indexed: 12/17/2022] Open
Abstract
In the last decade, the analysis of the medical images has evolved significantly, applications and tools capable to extract quantitative characteristics of the images beyond the discrimination capacity of the investigator's eye being developed. The applications of this new research field, called radiomics, presented an exponential growth with direct implications in the diagnosis and prediction of response to therapy. Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype with a severe prognosis, despite the aggressive multimodal treatments applied according to the guidelines. Radiomics has already proven the ability to differentiate TNBC from fibroadenoma. Radiomics features extracted from digital mammography may also distinguish between TNBC and non-TNBC. Recent research has identified three distinct subtypes of TNBC using IRM breast images voxel-level radiomics features (size/shape related features, texture features, sharpness). The correlation of these TNBC subtypes with the clinical response to neoadjuvant therapy may lead to the identification of biomarkers in order to guide the clinical decision. Furthermore, the variation of some radiomics features in the neoadjuvant settings provides a tool for the rapid evaluation of treatment efficacy. The association of radiomics features with already identified biomarkers can generate complex predictive and prognostic models. Standardization of image acquisition and also of radiomics feature extraction is required to validate this method in clinical practice.
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Affiliation(s)
- Camil Ciprian Mireștean
- Department of Oncology and Radiotherapy, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
- Department of Surgery, Railways Clinical Hospital, 700506 Iasi, Romania
| | - Constantin Volovăț
- Department of Medical Oncology-Radiotherapy, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (C.V.); (D.P.T.I.)
- Euroclinic Oncological Hospital, 700110 Iasi, Romania
| | - Roxana Irina Iancu
- Department of Oral Pathology, Faculty of Dentistry, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Clinical Laboratory Department, “St. Spiridon” Emergency Hospital, 700111 Iasi, Romania
- Correspondence: ; Tel.: +40-232-301-603
| | - Dragoș Petru Teodor Iancu
- Department of Medical Oncology-Radiotherapy, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (C.V.); (D.P.T.I.)
- Department of Radiotherapy, Regional Institute of Oncology, 700483 Iasi, Romania
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23
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Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, D'Ippolito E, Annunziata S, Farchione A, Belfiore MP, Desideri I, Cappabianca S. Delta radiomics: a systematic review. Radiol Med 2021; 126:1571-1583. [PMID: 34865190 DOI: 10.1007/s11547-021-01436-7] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/18/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. METHODS Eligible articles were searched in Embase, PubMed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with three key search terms: "radiomics", "texture", and "delta". Studies were analysed using QUADAS-2 and the RQS tool. RESULTS Forty-eight studies were finally included. The studies were divided into preclinical/methodological (five studies, 10.4%); rectal cancer (six studies, 12.5%); lung cancer (twelve studies, 25%); sarcoma (five studies, 10.4%); prostate cancer (three studies, 6.3%), head and neck cancer (six studies, 12.5%); gastrointestinal malignancies excluding rectum (seven studies, 14.6%), and other disease sites (four studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. CONCLUSIONS Delta radiomics shows potential benefit for several clinical endpoints in oncology (differential diagnosis, prognosis and prediction of treatment response, and evaluation of side effects). Nevertheless, the studies included in this systematic review suffer from the bias of overall low quality, so that the conclusions are currently heterogeneous, not robust, and not replicable. Further research with prospective and multicentre studies is needed for the clinical validation of delta radiomics approaches.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Luca Boldrini
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Giovanna Vacca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Emma D'Ippolito
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Salvatore Annunziata
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Alessandra Farchione
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Isacco Desideri
- Department of Biomedical, Experimental and Clinical Sciences "M. Serio", University of Florence, Florence, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
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24
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Wan L, Peng W, Zou S, Ye F, Geng Y, Ouyang H, Zhao X, Zhang H. MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Acad Radiol 2021; 28 Suppl 1:S95-S104. [PMID: 33189550 DOI: 10.1016/j.acra.2020.10.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/20/2020] [Accepted: 10/20/2020] [Indexed: 02/07/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate the capability of delta-radiomics to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS This retrospective study enrolled 165 consecutive patients with LARC (training set, n = 116; test set, n = 49) who received nCRT before surgery. All patients underwent pre- and post-nCRT MRI examination from which radiomics features were extracted. A delta-radiomics feature was defined as the percentage change in a radiomics feature from pre- to post-nCRT MRI. A data reduction and feature selection process including the least absolute shrinkage and selection operator algorithm was performed for building T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) delta-radiomics signature. Logistic regression was used to build a T2WI and DWI combined radiomics model. Receiver operating characteristic analysis was performed to assess diagnostic performance. Delong method was used to compare the performance of delta-radiomics model with that of magnetic resonance tumor regression grade (mrTRG). RESULTS Twenty-seven of 165 patients (16.4%) achieved pCR. T2WI and DWI delta-radiomics signature, and the combined model showed good predictive performance for pCR. The combined model achieved the highest areas under the receiver operating characteristic curves of 0.91 (95% confidence interval: 0.85-0.98) and 0.91 (95% confidence interval: 0.83-0.99) in the training and test sets, respectively (significantly greater than those for mrTRG; training set, p < 0.001; test set, p = 0.04). CONCLUSION MRI-based delta-radiomics can help predict pCR after nCRT in patients with LARC with better performance than mrTRG.
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25
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Zheng Y, Chen L, Liu M, Wu J, Yu R, Lv F. Nonenhanced MRI-based radiomics model for preoperative prediction of nonperfused volume ratio for high-intensity focused ultrasound ablation of uterine leiomyomas. Int J Hyperthermia 2021; 38:1349-1358. [PMID: 34486913 DOI: 10.1080/02656736.2021.1972170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
OBJECTIVES To develop and assess nonenhanced MRI-based radiomics model for the preoperative prediction of nonperfused volume (NPV) ratio of uterine leiomyomas after high-intensity focused ultrasound (HIFU) treatment. METHODS Two hundred and five patients with uterine leiomyomas treated by HIFU were enrolled and allocated to training (N =164) and testing cohorts (N = 41). Pyradiomics was used to extract radiomics features from T2-weighted images and apparent diffusion coefficient (ADC) map generated from diffusion-weighted imaging (DWI). The clinico-radiological model, radiomics model, and radiomics-clinical model which combined the selected radiomics features and clinical parameters were used to predict technical outcomes determined by NPV ratios where three classification groups were created (NPV ratio ≤ 50%, 50-80% or ≥ 80%). The receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration and decision curve analyses were performed to illustrate the prediction performance and clinical usefulness of model in the training and testing cohorts. RESULTS The multi-parametric MRI-based radiomics model outperformed T2-weighted imaging (T2WI)-based radiomics model, which achieved an average AUC of 0.769 (95% confidence interval [CI], 0.701-0.842), and showed satisfactory prediction performance for NPV ratio classification. The radiomics-clinical model demonstrated best prediction performance for HIFU treatment outcome, with an average AUC of 0.802 (95% CI, 0.796-0.850) and an accuracy of 0.762 (95% CI, 0.698-0.815) in the testing cohort, compared to the clinico-radiological and radiomics models. The decision curve also indicated favorable clinical usefulness of the radiomics-clinical model. CONCLUSIONS Nonenhanced MRI-based radiomics has potential in the preoperative prediction of NPV ratio for HIFU ablation of uterine leiomyomas.
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Affiliation(s)
- Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China.,State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing, China
| | - Liping Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
| | - Jiahui Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China.,State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing, China.,Medical Data Science Academy, Chongqing Medical University, Chongqing, China
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26
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Korte JC, Cardenas C, Hardcastle N, Kron T, Wang J, Bahig H, Elgohari B, Ger R, Court L, Fuller CD, Ng SP. Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer. Sci Rep 2021; 11:17633. [PMID: 34480036 PMCID: PMC8417253 DOI: 10.1038/s41598-021-96600-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
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Affiliation(s)
- James C. Korte
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XDepartment of Biomedical Engineering, University of Melbourne, Melbourne, Australia
| | - Carlos Cardenas
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Nicholas Hardcastle
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1007.60000 0004 0486 528XCentre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Tomas Kron
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XSir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Jihong Wang
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Houda Bahig
- grid.410559.c0000 0001 0743 2111Radiation Oncology Department, Centre Hospitalier de l’Université de Montréal, Montreal, Canada
| | - Baher Elgohari
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.10251.370000000103426662Clinical Oncology & Nuclear Medicine Department, Mansoura University, Mansoura, Egypt
| | - Rachel Ger
- grid.470142.40000 0004 0443 9766Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ USA
| | - Laurence Court
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Clifton D. Fuller
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Sweet Ping Ng
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.1055.10000000403978434Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia ,grid.482637.cDepartment of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Melbourne, Australia
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27
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Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell 2021; 39:916-927. [PMID: 33930310 PMCID: PMC8282694 DOI: 10.1016/j.ccell.2021.04.002] [Citation(s) in RCA: 114] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/25/2021] [Accepted: 04/03/2021] [Indexed: 12/22/2022]
Abstract
Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer care. With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of multi-dimensional data, deduce patterns, and predict outcomes to improve shared patient and clinician decision making. While there is high potential, significant challenges remain. In this perspective, we propose a pathway of clinical cancer care touchpoints for narrow-task AI applications and review a selection of applications. We describe the challenges faced in the clinical translation of AI and propose solutions. We also suggest paths forward in weaving AI into individualized patient care, with an emphasis on clinical validity, utility, and usability. By illuminating these issues in the context of current AI applications for clinical oncology, we hope to help advance meaningful investigations that will ultimately translate to real-world clinical use.
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Affiliation(s)
- Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Ahmed Hosny
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine - HIM 343, 77 Avenue Louis Pasteur, Boston, MA 02115, USA; Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands.
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28
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Lafata KJ, Chang Y, Wang C, Mowery YM, Vergalasova I, Niedzwiecki D, Yoo DS, Liu JG, Brizel DM, Yin FF. Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers. Med Phys 2021; 48:3767-3777. [PMID: 33959972 DOI: 10.1002/mp.14926] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/26/2021] [Accepted: 04/25/2021] [Indexed: 01/01/2023] Open
Abstract
PURPOSE This study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence-free survival (RFS). MATERIALS AND METHODS Sixty-four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB-approved study. Investigational 18 F-FDG-PET/CT images were acquired prior to treatment and 2 weeks (20 Gy) into a seven-week course of therapy. Fifty-five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naïvely compared to residual disease and/or subsequent recurrence and used to derive Kaplan-Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume. RESULTS While pre-treatment radiomics were not prognostic, intra-treatment radiomic expression was intrinsically associated with both residual/recurrent disease (P = 0.0256, χ 2 test) and RFS (HR = 7.53, 95% CI = 2.54-22.3; P = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26-5.76; P = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03-7.04; P = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches. CONCLUSIONS Our data illustrate the prognostic value of intra-treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow-up times, and unsupervised clustering formalism that is less prone to hyper-parameter tuning and over-fitting compared to supervised learning.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Yushi Chang
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Yvonne M Mowery
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Irina Vergalasova
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Donna Niedzwiecki
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - David S Yoo
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Jian-Guo Liu
- Department of Mathematics, Duke University, Durham, NC, USA.,Department of Physics, Duke University, Durham, NC, USA
| | - David M Brizel
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
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29
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Shayesteh S, Nazari M, Salahshour A, Sandoughdaran S, Hajianfar G, Khateri M, Yaghobi Joybari A, Jozian F, Fatehi Feyzabad SH, Arabi H, Shiri I, Zaidi H. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer. Med Phys 2021; 48:3691-3701. [PMID: 33894058 DOI: 10.1002/mp.14896] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES We evaluate the feasibility of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT). MATERIALS AND METHODS This retrospective study included 53 LARC patients divided into a training set (Center#1, n = 36) and external validation set (Center#2, n = 17). T2-weighted (T2W) MRI was acquired for all patients, 2 weeks before and 4 weeks after nCRT. Ninety-six radiomic features, including intensity, morphological and second- and high-order texture features were extracted from segmented 3D volumes from T2W MRI. All features were harmonized using ComBat algorithm. Max-Relevance-Min-Redundancy (MRMR) algorithm was used as feature selector and k-nearest neighbors (KNN), Naïve Bayes (NB), Random forests (RF), and eXtreme Gradient Boosting (XGB) algorithms were used as classifiers. The evaluation was performed using the area under the receiver operator characteristic (ROC) curve (AUC), sensitivity, specificity and accuracy. RESULTS In univariate analysis, the highest AUC in pre-, post-, and delta-radiomic features were 0.78, 0.70, and 0.71, for GLCM_IMC1, shape (surface area and volume) and GLSZM_GLNU features, respectively. In multivariate analysis, RF and KNN achieved the highest AUC (0.85 ± 0.04 and 0.81 ± 0.14, respectively) among pre- and post-treatment features. The highest AUC was achieved for the delta-radiomic-based RF model (0.96 ± 0.01) followed by NB (0.96 ± 0.04). Overall. Delta-radiomics model, outperformed both pre- and post-treatment features (P-value <0.05). CONCLUSION Multivariate analysis of delta-radiomic T2W MRI features using machine learning algorithms could potentially be used for response prediction in LARC patients undergoing nCRT. We also observed that multivariate analysis of delta-radiomic features using RF classifiers can be used as powerful biomarkers for response prediction in LARC.
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Affiliation(s)
- Sajad Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Salahshour
- Department of Radiology, Alborz University of Medical Sciences, Karaj, Iran
| | - Saleh Sandoughdaran
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular, Medical & Research Centre, Iran University of Medical Science, Tehran, Iran
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Yaghobi Joybari
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fariba Jozian
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.,Geneva University Neurocenter, Geneva University, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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30
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Anderson BM, Wahid KA, Brock KK. Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays. Pract Radiat Oncol 2021; 11:226-229. [PMID: 33607331 PMCID: PMC8102371 DOI: 10.1016/j.prro.2021.02.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 10/22/2022]
Abstract
Deep learning is becoming increasingly popular and available to new users, particularly in the medical field. Deep learning image segmentation, outcome analysis, and generators rely on presentation of Digital Imaging and Communications in Medicine (DICOM) images and often radiation therapy (RT) structures as masks. Although the technology to convert DICOM images and RT structures into other data types exists, no purpose-built Python module for converting NumPy arrays into RT structures exists. The 2 most popular deep learning libraries, Tensorflow and PyTorch, are both implemented within Python, and we believe a set of tools built in Python for manipulating DICOM images and RT structures would be useful and could save medical researchers large amounts of time and effort during the preprocessing and prediction steps. Our module provides intuitive methods for rapid data curation of RT-structure files by identifying unique region of interest (ROI) names and ROI structure locations and allowing multiple ROI names to represent the same structure. It is also capable of converting DICOM images and RT structures into NumPy arrays and SimpleITK Images, the most commonly used formats for image analysis and inputs into deep learning architectures and radiomic feature calculations. Furthermore, the tool provides a simple method for creating a DICOM RT-structure from predicted NumPy arrays, which are commonly the output of semantic segmentation deep learning models. Accessing DicomRTTool via the public Github project invites open collaboration, and the deployment of our module in PyPi ensures painless distribution and installation. We believe our tool will be increasingly useful as deep learning in medicine progresses.
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Affiliation(s)
- Brian M Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Kareem A Wahid
- UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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31
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Zhang Z, Huang M, Jiang Z, Chang Y, Torok J, Yin FF, Ren L. 4D radiomics: impact of 4D-CBCT image quality on radiomic analysis. Phys Med Biol 2021; 66:045023. [PMID: 33361574 DOI: 10.1088/1361-6560/abd668] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE To investigate the impact of 4D-CBCT image quality on radiomic analysis and the efficacy of using deep learning based image enhancement to improve the accuracy of radiomic features of 4D-CBCT. MATERIAL AND METHODS In this study, 4D-CT data from 16 lung cancer patients were obtained. Digitally reconstructed radiographs (DRRs) were simulated from the 4D-CT, and then used to reconstruct 4D CBCT using the conventional FDK (Feldkamp et al 1984 J. Opt. Soc. Am. A 1 612-9) algorithm. Different projection numbers (i.e. 72, 120, 144, 180) and projection angle distributions (i.e. evenly distributed and unevenly distributed using angles from real 4D-CBCT scans) were simulated to generate the corresponding 4D-CBCT. A deep learning model (TecoGAN) was trained on 10 patients and validated on 3 patients to enhance the 4D-CBCT image quality to match with the corresponding ground-truth 4D-CT. The remaining 3 patients with different tumor sizes were used for testing. The radiomic features in 6 different categories, including histogram, GLCM, GLRLM, GLSZM, NGTDM, and wavelet, were extracted from the gross tumor volumes of each phase of original 4D-CBCT, enhanced 4D-CBCT, and 4D-CT. The radiomic features in 4D-CT were used as the ground-truth to evaluate the errors of the radiomic features in the original 4D-CBCT and enhanced 4D-CBCT. Errors in the original 4D-CBCT demonstrated the impact of image quality on radiomic features. Comparison between errors in the original 4D-CBCT and enhanced 4D-CBCT demonstrated the efficacy of using deep learning to improve the radiomic feature accuracy. RESULTS 4D-CBCT image quality can substantially affect the accuracy of the radiomic features, and the degree of impact is feature-dependent. The deep learning model was able to enhance the anatomical details and edge information in the 4D-CBCT as well as removing other image artifacts. This enhancement of image quality resulted in reduced errors for most radiomic features. The average reduction of radiomics errors for 3 patients are 20.0%, 31.4%, 36.7%, 50.0%, 33.6% and 11.3% for histogram, GLCM, GLRLM, GLSZM, NGTDM and Wavelet features. And the error reduction was more significant for patients with larger tumors. The findings were consistent across different respiratory phases, projection numbers, and angle distributions. CONCLUSIONS The study demonstrated that 4D-CBCT image quality has a significant impact on the radiomic analysis. The deep learning-based augmentation technique proved to be an effective approach to enhance 4D-CBCT image quality to improve the accuracy of radiomic analysis.
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Affiliation(s)
- Zeyu Zhang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
| | - Mi Huang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America
| | - Zhuoran Jiang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,School of Electronic Science and Engineering, Nanjing University, 163 Xianlin Road, Nanjing, Jiangsu, 210046, People's Republic of China
| | - Yushi Chang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
| | - Jordan Torok
- Department of Radiation Oncology, University of Pittsburgh Medical Center, 5150 Centre Ave, Pittsburgh, PA 15232, United States of America
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America.,Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316, People's Republic of China
| | - Lei Ren
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
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32
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Porcu M, Solinas C, Mannelli L, Micheletti G, Lambertini M, Willard-Gallo K, Neri E, Flanders AE, Saba L. Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians. Crit Rev Oncol Hematol 2020; 154:103068. [PMID: 32805498 DOI: 10.1016/j.critrevonc.2020.103068] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/13/2020] [Accepted: 07/23/2020] [Indexed: 02/06/2023] Open
Abstract
In recent years the concept of precision medicine has become a popular topic particularly in medical oncology. Besides the identification of new molecular prognostic and predictive biomarkers and the development of new targeted and immunotherapeutic drugs, imaging has started to play a central role in this new era. Terms such as "radiomics", "radiogenomics" or "radi…-omics" are becoming increasingly common in the literature and soon they will represent an integral part of clinical practice. The use of artificial intelligence, imaging and "-omics" data can be used to develop models able to predict, for example, the features of the tumor immune microenvironment through imaging, and to monitor the therapeutic response beyond the standard radiological criteria. The aims of this narrative review are to provide a simplified guide for clinicians to these concepts, and to summarize the existing evidence on radiomics and "radi…-omics" in cancer immunotherapy.
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Affiliation(s)
- Michele Porcu
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy.
| | - Cinzia Solinas
- Medical Oncology, Azienda Tutela Salute Sardegna, Hospital Antonio Segni, Ozieri, SS, Italy
| | | | - Giulio Micheletti
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy
| | - Matteo Lambertini
- Department of Medical Oncology, U.O.C. Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genova, Italy; Department of Internal Medicine and Medical Specialties (DiMI), School of Medicine, University of Genova, Genova, Italy
| | | | | | - Adam E Flanders
- Department of Radiology, Division of Neuroradiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Luca Saba
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy
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